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In this age of technology and innovation, the K-12 learning is continually evolving in order to adapt to the times. It’s important that teachers keep up-to-date with the latest helpful technology for their students and that parents understand the ramifications of that technology, too.

Here are six major classroom trends that are impacting the K-12 learning process:

1. BYOD : This movement which embraces mobile technology through the devices that students already own has already gained momentum in many districts across the country, and it is rapidly moving towards mass adoption. Places like Chesapeake Public Schools are already allowed to use privately owned electronic device s to access the wireless network on the school system’s filtered Internet. In Chesapeake, as in the other public and private schools where BYOD policies exist, students must sign a responsibility form that says they will only use the mobile device for academic enrichment while on school property. Students who bring their own devices into the classroom eliminate the initial costs and are also already comfortable with the technology. The downside of course is that not all students can readily afford such technology, but look for schools to develop technology financial assistance programs for families to help offset the full cost and maintenance of school-owned devices.

2. Customized learning experiences : Self-initiated and self-directed learning experiences are based upon individual needs, preferences and abilities of students who are then the masters of their own success.  The traditional way to look at learning is through teachers creating and assigning all work for students in a one-size-fits-all approach.

On the flip side, customized learning has the ability to incorporate a variety of resources, such as virtual learning, to aid in the learning process while making it a way for teachers to moderate one-on-one learning experiences in practical ways. I think that the idea of handing control to students is hypothetically frightening to some educators and administrators but once they’ve actually tried it on a small scale, personalized learning actually looks more attractive from an adult’s perspective.

3. Online learning : Virtual learning is certainly not new to the K-12 scene, but its increasing popularity can’t be ignored. It used to be that online learning was associated only with distance learning, or students who went through the academic process off of school grounds. Today, online learning is more segmented and often just one part of a more traditional learning experience. Virtual learning is no longer all or nothing; it has become mainstream and will continue to transform in-classroom learning.

4. Early education emphasis : Optional preschool is quickly becoming a thing of the past. Research shows that students who start the formal education experience, even one year earlier than Kindergarten, fare better long term in their academic careers. Thirty eight states offer free, voluntary preschool learning programs and nearly 1.6 million low-income families receive assistance from the federal Child Care Development Fund to pursue early childhood education. That fund is just one portion of President Obama’s $75 billion plan to expand early childhood learning in order to give American students a stronger foundation going into Kindergarten. I expect that in the next decade, our terminology will change from K-12 to PK-12 when we talk about student benchmarks.

5. Outdoor/environmental learning : In short, more schools are looking for ways to get students and teachers outside. We are in an era of experiential learning, so environmental education fits the bill for many students.  Lessons in this field teach children an appreciation of the earth and of its resources that the human population is quickly depleting. A better, hands-on understanding of nature also helps with science comprehension and gives students practical learning experiences.

Research has also found that teaching outside, even for short stints, improves student attitudes, attendance and overall health . In many schools teachers have always had the freedom to take students outside if they deemed it lesson-appropriate.

6. Strengthening STEM education : A greater focus on science, technology, engineering and math (STEM) learning has been a “trend” for at least the better part of a decade.  Specifically, teachers are looking for innovative ways to deliver STEM material (mobile technology is just one way, virtual science labs are another) and more stringent benchmarks are being created at the local, state and federal level. It is no longer enough for American students to just get by in comparison to each other in STEM subjects; global competition is proving that students in the U.S. need more focus in these subjects to lead the worldwide marketplace as adults.

As you can see, K-12 classrooms are not at all static, but will continue to incorporate cutting-edge technology and socially relevant practices into the curriculum. Understanding and supporting this technology is vital to advancing K-12 students to the next level of their educational success.

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New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

Classroom Q&A

With larry ferlazzo.

In this EdWeek blog, an experiment in knowledge-gathering, Ferlazzo will address readers’ questions on classroom management, ELL instruction, lesson planning, and other issues facing teachers. Send your questions to [email protected]. Read more from this blog.

‘My Online Learning Experience as a Student This Fall Has Been Great’

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This series highlights contributions from students in my classes.

In Part One , Cathy Liu, Julia Yang, Eliseo Angulo Lopez, and Masihullah Shafiq shared their thoughts.

In Part Two , Luis Diaz, Samantha Nicole Vicedo, Cheyenne Lo, and Manpreet Rana contribute their commentaries.

Today, Lyna Nguyen, Nono Loek, and Rachel Anjel “wrap up” this series...

Teachers ‘have been really understanding’

Lyna Nguyen is a junior at Luther Burbank High School in Sacramento, Calif.

My online learning experience as a student this fall has been great. What’s working for me is I like the 40 minutes in class and 40 minutes asynchronous time to work on our assignments. I feel like teachers have been really understanding and helpful of what we’re going through and how difficult it can be. I find it more relaxing for me because everything is organized through Google Classroom, and it puts a little stress off of me because I disliked feeling disorganized and anxious.

What is not working for me is sometimes my time management is off, and it can be a little stressful how teachers assign work really close to the due date of other class assignments. I’d rather have the assignments’ due dates spread out throughout the week. Sometimes, there are technology difficulties, which can interrupt my learning experience. Everything is also online, so it can take a while communicating with teachers. Overall, communication is delayed, and it can be difficult reaching out to teachers and asking for assistance. Even when I need help with a certain assignment, it can be difficult learning and processing information through a screen. Other than that, I appreciate teachers trying their best to make everything work during this pandemic.

‘Internet classes save time’

Nono Loek is a senior at Luther Burbank High School:

In my experience with internet classes, the good far outweighs the bad. Internet classes save time, and saving time is important to me. The studies are structured much better than I expected. I don’t really see a difference between the online and offline structures. Group work is done in separate rooms, and I think it’s not that bad, because we also learn a lot in online classes.

I sometimes miss the interaction with people, making long-lasting friendships, which is extremely important for all of us. At home, it’s hard to force yourself to take a break. I think this pandemic has t probably transformed education. Most of the things we found to be impossible have now proven to be possible.

I actually really enjoy distance learning, but sometimes I want everything to go back to normal. I also like how I don’t have to wake up at 6 every morning. I like that with learning online, I can relax more, as well as think more. What I don’t like about online learning is that it can take me up to a whole school day (6-7 hours) to finish assignments.

‘I understand teachers are trying to keep us safe’

Rachel Anjel is a senior at Luther Burbank High School:

My online experience this year is kind of boring because I don’t really get to see friends in school anymore. And I thought it was going to be fun using Chromebooks. Sometimes it can be difficult because sometimes the Wi-Fi doesn’t work or it can be slow. I sometimes feel bored because I can’t spend or hang out with my friends.

A good thing is that we have this app called FaceTime, so whenever I’m bored, I can just call my friends. We talk and play while we do our homework and have fun. But at the same time, I think having an online school is bad because I hate sitting in my room the whole six hrs. I remember saying I like online school, but now it’s getting boring. I think it would be better if we went to school. But I understand teachers are trying to keep us safe.

Thanks to Lyna, Nono, and Rachel for their contributions!

(This is the final post in a multipart series. You can see Part One here and Part Two here .)

The question-of-the-week:

What has your online learning experience been as a student this fall? What is working for you and why? What is not working for you and why?

Consider contributing a question to be answered in a future post. You can send one to me at [email protected] . When you send it in, let me know if I can use your real name if it’s selected or if you’d prefer remaining anonymous and have a pseudonym in mind.

You can also contact me on Twitter at @Larryferlazzo .

Education Week has published a collection of posts from this blog, along with new material, in an e-book form. It’s titled Classroom Management Q&As: Expert Strategies for Teaching .

If you missed any of the highlights from the first eight years of this blog, you can see a categorized list below. The list doesn’t include ones from this current year.

• This Year’s Most Popular Q&A Posts
• Race & Racism in Schools
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• Classroom-Management Advice
• Best Ways to Begin the School Year
• Best Ways to End the School Year
• Student Motivation & Social-Emotional Learning
• Implementing the Common Core
• Facing Gender Challenges in Education
• Teaching Social Studies.
• Cooperative & Collaborative Learning
• Using Tech in the Classroom
• Student Voices
• Parent Engagment In Schools
• Teaching English-Language Learners
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I am also creating a Twitter list including all contributors to this column

The opinions expressed in Classroom Q&A With Larry Ferlazzo are strictly those of the author(s) and do not reflect the opinions or endorsement of Editorial Projects in Education, or any of its publications.

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Accountability in US Education: Applying Lessons from K-12 Experience to Higher Education

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• March 24, 2022

The future of education hinges on the rapid advancement of technology made evident by the events that unfolded during the COVID-19 pandemic. We learned how educational technology, or ed-tech, could bridge the gap as we passed through it. 

If you think advancements in edtech are reserved for higher education, you are wrong.

These changes are going to leave their impact on all stages of the modern-day education system including K12.

If you have a child in K12 or look to improve children’s education even further, this should interest you. Here’s how k12 ed-tech is changing and how this affects your kid’s future.

Ed-Tech Underscores Differentiated Instruction

One of the best benefits of Ed-tech is its ability to emphasize differentiated instruction. The idea behind differentiated instruction is simple: you want to tailor the curriculum to facilitate learning. This lets everyone be on the same page, regardless of their learning capability.

Students have different paces and a variety of learning processes. Using this approach includes everyone, from average learners to struggling individuals, learning-disabled students, and even gifted individuals. 

Ed-Tech makes instruction simpler with software that can make classes more interactive. The online learning environment can help shy kids join in the discussion, while more visual learners can maximize interactive software.

Differentiated instruction also helps children with learning disabilities to stay competitive. Kids with learning disabilities would need a different approach compared to normal kids. Interactive, visual content from tech can help.

The Power of Video in Learning

Online video is one of the most powerful forms of K12 ed-tech, as the medium features multiple advantages. It allows students and teachers to brush up on their skills anytime. While students shunned tape sessions back in the 90s, video became a more powerful tool.

Time efficiency is an essential benefit of online videos. Students can view the video at their own pace, making it perfect for busy students and those who need to refresh what they know. Learning videos also offer better knowledge retention due to the change in perspective.

Additionally, videos have been found to enhance students’ critical thinking skills. Students can view practical demonstrations through videos. Through these, students can learn directly from experts in their fields. 

Videos provide students with real-life experiences and highlight information that would have been impossible to convey effectively.

Classroom Management Tools Improve Teaching Efficiency

Classroom management is one of the most critical aspects of teaching. A key challenge in a classroom is managing class size. The bigger the class size, the less focus teachers can put towards better communication.

Class management tools such as classroom management software and scheduling systems help teachers manage the classroom more effectively. These digital tools allow instructors to keep track of attendance, grades, and student progress. 

Such ed-tech improves the sharing of learning materials while creating a safer teaching environment for students. It also allows teachers to provide better group feedback and which areas need better focus. This leads to better efficiency down the line.

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Ed-Tech Maximizes Collaboration

Today, online learning has become an integral part of the school curriculum. Students use computers more than ever, and learning is more effective through online collaboration. Online collabs let kids work together from their homes, keeping them safe with all the educational resources they need.

These online platforms allow students to work individually or in groups. They can chat with classmates and professors for instant feedback and collaborate on projects and assignments. This increases both productivity and engagement among students.

As ed-tech improves, collaboration can even bring students from other parts of the world. It can allow for cross-cultural exchanges and even have kids who can’t go to school to work remotely with their peers.

Interactive Technology Educates Better

The goal of 21st-century technology is to improve learning experiences. This starts with the interaction students have with their lessons. Interactive education is helping students learn more efficiently while motivating them further.

With omnichannel e-learning, students can experience the same lesson in different ways. They can watch a video, read a text, take a quiz, and go through real-world scenarios. They can even play games geared towards maximizing their lessons and letting them use their senses.

The variety of tools helps cater to different learning styles and interests. This also helps bridge the gaps between technical, academic, and career-oriented skills. Teachers can use these tools to create lessons tailored to individual preferences.

Preserving Creativity With Tech

Modern-day e-learning is transforming the way children understand the world. Technology has given educators new ways and ideas to enhance their lesson plans creatively. Educators can now create their own content from scratch and share it with the rest of the world.

Such pedagogies can foster creativity and critical thinking, with endless options. They can pursue their passions and hone their craft, giving them the confidence to pursue their dreams. Rather than stifle children’s creative skills, ed-tech helps build it even further into other media.

Encouraging Inclusivity

One of the biggest problems in education is the large divide between the haves and the have-nots that persists today. The gap between schools and poor communities can stunt the growth and development of many.

With growing ed-tech, schools can now use online resources and extend their services to those who would otherwise not have access to it. Instead of relying on physical infrastructure, e-platforms can be accessible from anywhere. This means more opportunities for underserved and remote areas. 

At the same time, it allows children to learn more about various non-academic topics. These include gender identity, feminism, race, and more.

Ed-Tech Maximizes Resource Access For All

Education has always been a priority of governments worldwide. However, it’s difficult for them to deliver quality outcomes with limited resources. This results in a lack of access to quality education. With brick-and-mortar classrooms, it can be hard for remote locations to get their hands-on educational materials. 

On the other hand, conferencing and technology allow for more resource sharing. This allows remote students to get the most out of their settings. They can access online courses and collaborate with other students. 

This also means that families can balance work and home obligations. They can educate their kids through 2-way video conferencing, allowing them to catch up on lectures and homework while they’re still at work. This cuts down on unnecessary stress and helps kids continue learning outside the class.

The Bottom Line

The advent of 21st-century technology has changed the way we learn. These innovations make the learning process more engaging and effective. As we navigate through the challenges of the pandemic and beyond, it’s essential to focus on how we can leverage these new technologies to educate the next generation. 

Ed-tech is changing the way we learn. It helps us adapt to the ever-changing world of K-12 education, helping kids of all ages and backgrounds gain new skills and acquire new knowledge. Whether you’re a teacher or a student, technological improvements can help you maximize every learning opportunity.

Edly and K12 Education

Having an effective and powerful K12 LMS is what can distinguish high-quality learning experiences from the rest. Edly’s K12 Learning Management System is well-equipped to handle the demanding educational needs that change as the years progress. Edly responds to the audience’s needs of all ages and competence levels by building systems for them that fulfill the requirements of the organizations.

Edly is the largest and amongst the earliest technology and service partners for Open edX . It has been working with edX since 2013 with an aim to build comprehensive Open edX learning environments. Through its e-learning ecosystem, Edly helps you create your own, personalized K12 learning experience and build a seamless solution.  Request a free demo  today and let the years of experience in high-value online learning solutions of Edly’s experts do great things for you and your learners.

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New $90 Million Project to Create Digital Research Hub Focused on K-12 Education

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The National Science Foundation has awarded Rice University $90 million to build what is being described as a first-of-its-kind education research hub that will leverage data from an array  of major digital learning platforms currently serving tens of millions of students. 

The university recently announced the investment as its largest ever federal research grant. OpenStax at Rice, a major publisher of open education resources, will build the research and development hub known as SafeInsights .

The project will focus on producing “research-informed insights about teaching and learning for educators, institutions and learning platforms to use to create tailored programs, pedagogies and policies that will equip learners to thrive.”

“Just like there are bigger telescopes that let astronomers see deeper into the night, SafeInsights’ goal is to have this large student population that will enable researchers to see deeper into the student learning experience,” Slavinsky said in an interview. 

The SafeInsights hub will take five years to build, he said, with early research projects b eginning in 16 to 18 months, and full-scale research operations starting in 2029. 

School districts’ commitment to seeking and using research-based educational strategies is uneven at best. Many district officials complain that academic and other scientifically based research is too abstract, disconnected from their work, and outdated to be of practical use in their decision-making.

When surveyed recently by EdWeek Market Brief on what sorts of research they value most when choosing products and services, district and school leaders were much more likely to point to data on student outcomes, or product usage data than they were rigorous, experimental research.

Data Security in Focus 

At $90 million, the award is NSF’s largest investment in research and development infrastructure for education at a national scale, the university said.

In the past, education research has been hampered by small study groups and short time frames, but the recent boom in digital learning can provide researchers with a plethora of data needed to better understand academic outcomes, Slavinsky said. 

SafeInsights’ goal is to have this large student population that will enable researchers to see deeper into the student learning experience. J.P. Slavinsky, Executive Director, SafeInsights

And research will not be limited to only STEM (science, technology, engineering, and math) areas. Schools, education companies, and researchers participating in SafeInsights will bring their own research agenda, Slavinsky said.

Protecting student data is a major concern within school districts, particularly as  schools’ and students’ reliance on technology  has steadily grown.

The data collected as part of the new project will remain secure, Slavinsky said. No student information will be revealed to researchers. 

Instead, researchers will submit their inquiries to SafeInsights, and the research hub will have the data in question analyzed where it is originally stored — by schools or on a digital platform — and provide researchers with aggregate results. 

The research and development project will be a central hub for 80 partners and collaborating institutions. That number is expected to grow, Slavinsky said. 

“One of the great things about SafeInsights is that it is very scalable,” he said. “And we want to build this community, so we can get a better and better picture of the students and the teachers we’re trying to help.” 

Follow  EdWeek Market Brief  on Twitter  @EdMarketBrief  or connect with us on  LinkedIn .

Image credit Stockyarder/DigitalVision/Getty

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2024 Best Schools in Santa Clara

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1-25 of 384 results

Castilleja School

Palo Alto, CA •

Private School •

• • Rating 4.39 out of 5   67 reviews

Parent: Our daughter arrived at Castilleja for 6th grade, and she is now finishing 7th grade -- and she thrived both years! She has developed a diverse group of close friends, and she has loved her teachers. As her parents, we required her to take Mandarin starting in 6th grade (one of the only things we've ever required her to do against her will), and she has absolutely loved her Mandarin teacher and the language as a result. Similarly, she disliked Math going into 6th grade, but now she loves her two math teachers and the subject. Castilleja provides an incredible education beyond traditional education. Casti teaches girls how to learn, how to manage their time, how to self advocate, executive functions, SEL, and how to behave socially. In addition, teachers and administration constantly focus the culture on the school's core values: Courage, Conscience, Courtesy, Charity, and Character. Most importantly, Castilleja's education and culture excels at producing strong women, strong leaders. ... Read 67 reviews

• grade  A+ Overall Niche Grade

Students 424

Student-teacher ratio 7:1

#5 Best Private High Schools in California .

Blue checkmark.

Private School ,

PALO ALTO, CA ,

67 Niche users give it an average review of 4.4 stars.

Featured Review: Parent says Our daughter arrived at Castilleja for 6th grade, and she is now finishing 7th grade -- and she thrived both years! She has developed a diverse group of close friends, and she has loved her teachers.... .

Read 67 reviews.

Overall Niche Grade : A+ ,

Students : 424 ,

Student-Teacher Ratio : 7 to 1 ,

The Harker School

San Jose, CA •

• • Rating 4.26 out of 5   179 reviews

Middle school student: Sometimes there can be a lot of pressure starting at a middle school level. However, I think this is just pushing us to be better students, and it prepares us for college. Also, the administration does not follow the student handbook rules that well. There were several occasions where punishments could have been a lot more severe, so in that aspect Harker may not be a school where you want to send your kids if you want them to learn from there mistakes. On the other hand, Harker has exceptional academics and they are a well -rounded school as they are good in some specific sports like swimming, volleyball, track, ect. ... Read 179 reviews

Students 1,982

Student-teacher ratio 9:1

#30 Best Private High Schools in California .

SAN JOSE, CA ,

179 Niche users give it an average review of 4.3 stars.

Featured Review: Middle school student says Sometimes there can be a lot of pressure starting at a middle school level. However, I think this is just pushing us to be better students, and it prepares us for college. Also, the administration... .

Read 179 reviews.

Students : 1,982 ,

Student-Teacher Ratio : 9 to 1 ,

Khan Lab School

Mountain View, CA •

• • Rating 4.78 out of 5   32 reviews

Senior: Small class sizes, creative end-of-term projects, collaborative learning environment. Students are incredibly self-driven academically and the education model encourages students to dive deep into what they are interested in. There are countless opportunities available to students if they choose to take advantage of them. Teachers are always willing to meet with students one-on-one or mentor students in projects (Capstone and Cornerstone programs are both great opportunities); they are super supportive and are dedicated to the school and the students. Because it is such a small school, everyone knows everyone else. The school culture can be cliquey and it can be especially difficult if you don't follow the class norm. The current culture of the school, from my experience, seems to revolve around the Robotics club and advanced courses in STEM. While the art and humanities courses are wonderful, the school culture doesn't view them as favorably as engineering/cs/math disciplines. ... Read 32 reviews

Students 286

Student-teacher ratio 6:1

#39 Best Private High Schools in California .

MOUNTAIN VIEW, CA ,

32 Niche users give it an average review of 4.8 stars.

Featured Review: Senior says Small class sizes, creative end-of-term projects, collaborative learning environment. Students are incredibly self-driven academically and the education model encourages students to dive deep into... Because it is such a small school, everyone knows everyone else. The school culture can be cliquey and it can be especially difficult if you don't follow the class norm. The current culture of the... .

Read 32 reviews.

Students : 286 ,

Student-Teacher Ratio : 6 to 1 ,

Challenger School - Strawberry Park

SAN JOSE, CA •

• • Rating 1 out of 5   1

German International School of Silicon Valley - Mountain View Campus

MOUNTAIN VIEW, CA •

• • Rating 4.29 out of 5   7

Bellarmine College Preparatory

• • Rating 4.55 out of 5   223

Sacred Heart Schools Atherton

Atherton, CA •

• • Rating 4.51 out of 5   110 reviews

Parent: We have sent both of our kids to Sacred Heart Schools for middle school and high school. Sacred Heart provides an excellent, well-rounded education, including academics, sports, arts, extra-curriculars, and community. Particularly impressive in terms of sports (for a smaller school) and college prep/admissions. ... Read 110 reviews

Students 639

Student-teacher ratio 4:1

#48 Best Private High Schools in California .

ATHERTON, CA ,

110 Niche users give it an average review of 4.5 stars.

Featured Review: Parent says We have sent both of our kids to Sacred Heart Schools for middle school and high school. Sacred Heart provides an excellent, well-rounded education, including academics, sports, arts,... .

Read 110 reviews.

Students : 639 ,

Student-Teacher Ratio : 4 to 1 ,

BASIS Independent Silicon Valley

• • Rating 3.92 out of 5   135 reviews

Senior: BASIS Independent Silicon Valley (BISV) has been a great school with a huge workload. Although it is sometimes hard to keep a work-life balance, you tend to learn over time how to manage your time under constant pressure. You learn to make time for great extracurriculars which can benefit your college apps while also keeping up your grades with teachers after school in mandatory office hours. I would like to see change in the overly competitive environment as it sometimes way too harsh on my peers and even myself, because grades become a competition even though the school tries to avoid it. This causes severe anxiety in its students and emotional breakdowns at times of failure in performance. If this changes, and a field is somehow added to our campus, BISV is GOLD. ... Read 135 reviews

Students 1,190

Student-teacher ratio 11:1

#49 Best Private High Schools in California .

135 Niche users give it an average review of 3.9 stars.

Featured Review: Senior says BASIS Independent Silicon Valley (BISV) has been a great school with a huge workload. Although it is sometimes hard to keep a work-life balance, you tend to learn over time how to manage your time... .

Read 135 reviews.

Students : 1,190 ,

Student-Teacher Ratio : 11 to 1 ,

Pinewood School - Upper Campus

Los Altos Hills, CA •

• • Rating 4 out of 5   34 reviews

Senior: This school allows students to have lots of access to resources and the the teacher to student count is relatively low. The education is good and I would go here again if I could. ... Read 34 reviews

Students 612

Student-teacher ratio 15:1

#71 Best Private High Schools in California .

LOS ALTOS HILLS, CA ,

34 Niche users give it an average review of 4 stars.

Featured Review: Senior says This school allows students to have lots of access to resources and the the teacher to student count is relatively low. The education is good and I would go here again if I could. .

Read 34 reviews.

Students : 612 ,

Student-Teacher Ratio : 15 to 1 ,

• • Rating 4.38 out of 5   40 reviews

Parent: Kehillah High School lives up to its name, which means 'community'. My son’s experience has been overwhelmingly positive, experiencing an academic program that not only challenges but also deeply engages the students in critical thinking and respectful discourse. The school is exceptional at intertwining rigorous academics with the nurturing values of respect, justice, and community support. The inclusivity is remarkable—students from all faiths are welcomed and embraced, enriching the learning experience for everyone. It’s been gratifying to see our child grow both academically and also as a conscientious human being. Kehillah is a place where students don't just prepare academically for the future but also learn to be thoughtful and engaged citizens. One improvement I’d wish for would be to have more people aware of what a rare gem Kehillah is - great academics and supportive environment. ... Read 40 reviews

Students 189

Student-teacher ratio 5:1

#75 Best Private High Schools in California .

40 Niche users give it an average review of 4.4 stars.

Featured Review: Parent says Kehillah High School lives up to its name, which means 'community'. My son’s experience has been overwhelmingly positive, experiencing an academic program that not only challenges but also deeply... The school is exceptional at intertwining rigorous academics with the nurturing values of respect, justice, and community support. The inclusivity is remarkable—students from all faiths are welcomed... Kehillah is a place where students don't just prepare academically for the future but also learn to be thoughtful and engaged citizens. One improvement I’d wish for would be to have more people aware of what a rare gem Kehillah is - great academics and supportive environment. .

Read 40 reviews.

Students : 189 ,

Student-Teacher Ratio : 5 to 1 ,

• • Rating 4.55 out of 5   223 reviews

Senior: Bellarmine is an amazing school in all the amazing programs and activities within and beyond the classroom. It offers an array of co-curricular activities ranging from the world's best in robotics, national level speech and debate, and state champions in football. Teachers, staff, and even other students are always on the lookout to help anyone they encounters. The school's culture is a crossroads of values like faith, justice, equality, and recognition of privilege. While teaching students the skills they'll need to use in their life in a professional environment or just in dealing with other people, Bellarmine College Preparatory goes above and beyond in all the ways one can think of. ... Read 223 reviews

Students 1,667

Student-teacher ratio 14:1

#78 Best Private High Schools in California .

223 Niche users give it an average review of 4.6 stars.

Featured Review: Senior says Bellarmine is an amazing school in all the amazing programs and activities within and beyond the classroom. It offers an array of co-curricular activities ranging from the world's best in robotics,... .

Read 223 reviews.

Students : 1,667 ,

Student-Teacher Ratio : 14 to 1 ,

Silicon Valley International School

• • Rating 4.5 out of 5   56 reviews

Parent: Our children are in the High School. They are thriving, and come home telling us about things they have learned in school. Academics are demanding because the school aims to prepare students for college academics with the IB program. There are study halls after school with teachers who offer tutoring in all subjects. My children enjoy the after-school clubs, and have had life-changing experiences with international programs. The teachers are accessible; when I have questions or want to consult them, they make arrangements right away to meet with me. The administration elicits feedback from parents and responds to ideas for the school. We are glad we found SVINTL in time for our children to attend the High School. ... Read 56 reviews

Students 714

#83 Best Private High Schools in California .

56 Niche users give it an average review of 4.5 stars.

Featured Review: Parent says Our children are in the High School. They are thriving, and come home telling us about things they have learned in school. Academics are demanding because the school aims to prepare students for... .

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The interplay between teachers’ trust in artificial intelligence and digital competence

• Open access
• Published: 20 May 2024

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• Margarida Lucas   ORCID: orcid.org/0000-0002-7438-5287 1 ,
• Yidi Zhang 1 ,
• Pedro Bem-haja 2 &
• Paulo Nuno Vicente 3  

This study examines the relation between K-12 teachers’ trust in artificial intelligence (TAI), their knowledge of AI (KAI), and their digital competence (DC). It further examines the relation between TAI and age, sex, teaching experience and International Standard Classification of Education (ISCED) levels. The study employed a comprehensive and validated instrument and used a sample of 211 primary and secondary school teachers. The results show that there is a significant positive relation between all three variables and that KAI is a robust and substantial predictor of TAI. In the absence of KAI, the significant relation between DC and TAI ceases to exist. In addition, teachers with different levels of DC do not show significant differences in their attitudes towards AI. Results further show that TAI is independent of age, sex, teaching experience and ISCED level in this sample of teachers. The study contributes valuable insights into the complex interplay between teachers’ TAI, their KAI, and their DC, providing practical implications for policy, teacher preparation and professional development in the rapidly evolving landscape of AI integration in education.

Avoid common mistakes on your manuscript.

1 Introduction

While the theoretical and technological foundations of Artificial Intelligence (AI) have existed since the 1950s, the convergence of various factors, such as the increased computational power, extensive availability of data, and sophisticated machine learning algorithms, has resulted in remarkable advancements. The widespread integration of AI into various aspects of our daily social life raises a pressing question about how to educate citizens and prepare them for a world full of intelligent technological devices. At the same time, it places increasing pressure on teachers, who are expected to not only gain knowledge and understanding of AI but also acquire new competences enabling them to teach about AI, for AI and with AI (Holmes et al., 2022 ; Kim & Kwon, 2023 ).

The relationship between digital competence and AI seems indisputable, as digital competence plays a pivotal role in the effective educational use and adoption of digital technologies, including those based on AI (Ng et al., 2023 ; Polak et al., 2022 ; Velander et al., 2023 ). However, empirical studies exploring the interaction between digital competence and AI adoption are, to the best of our knowledge, limited and therefore grant further research.

Teachers play a pivotal role in the AI integration in K-12 education (Casal-Otero et al., 2023 ), and are recognized as the largest user group of AI technology (Crompton et al., 2022 ). They are crucial to the successful adoption of AI systems and the realization of the potential benefits of digital data in education (European Commission, 2022 ). Notwithstanding, the topic of AI is relatively new to both students and teachers (Lindner & Berges, 2020 ), and as a result, the attitudes of teachers may oscillate between complete resistance and overconfidence (Casal-Otero et al., 2023 ). Nevertheless, such attitudes have a significant impact on the effectiveness of AI integration in education, as if teachers perceive the AI technology as detrimental to learning, they may either refrain from adopting it or use it in a negative way (Crompton et al., 2022 ).

The current situation reveals that teachers have limited knowledge and understanding of what AI is, and consequently how it could be used in education (Chounta et al., 2022 ; Hrastinski et al., 2019 ; Ng et al., 2023 ). Moreover, they express uncertainty about the necessary competences for effective use (Velander et al., 2023 ) and their understanding of AI related knowledge lacks empirical evidence (Chounta et al., 2022 ). Furthermore, some AI applications may lack transparency, which leads to concerns about accountability and trust (European Commission, 2022 ). Trust, as it is well reported in literature, plays a pivotal role in the adoption of any technology (Nazaretsky et al., 2021 ; Qin et al., 2020 ), but few studies have explored K-12 teachers’ trust in integrating AI based technologies into their teaching practices (Ayanwale et al., 2022 ; Kim & Kwon, 2023 ; Nazaretsky et al., 2021 , 2022 ; Su et al., 2023 ). In addition, the interplay between trust in AI and different variables is currently understudied. Against this background, the present study seeks to identify the variables that may influence K-12 teachers’ trust in AI. The study aims to provide practical implications for teacher preparation and in-service professional development regarding AI and implementation of AI tools and processes in K-12 education. The research aims to answer the following questions:

RQ1: What is the relation between trust in AI, knowledge of AI and digital competence?

RQ2: What is the relation between trust in AI, age, sex, teaching experience and International Standard Classification of Education (ISCED) levels?

1.1 Teachers’ digital competence and artificial intelligence in education

Teachers’ digital competence can be broadly defined as the individual teacher’s proficiency in the critical and reflective use of digital technologies in the various dimensions of their profession, with the pedagogical dimension being a core aspect. Frameworks like TPACK, the UNESCO ICT Competency Framework or DigCompEdu – to mention just a few of the most adopted - guide educators on the use of digital technology to improve teaching and learning.

Around the world, studies looking at teachers’ digital competence point at low levels and identify gaps to be addressed (Cabero-Almenara et al., 2022 ; Diz-Otero et al., 2023 ; Lucas et al., 2021 ; Runge et al., 2023 ; Tomczyk et al., 2023 ; Tzafilkou et al., 2023 ). In response, but also in addition to the challenges brought by the pandemic, international organizations and national governments have launched measures and initiatives with the main goal of promoting the development of teachers’ digital competences (e.g., Council of Ministers of Portugal, 2020 ; European Commission, 2020 ; Estonian Education and Research Ministry, 2021 ). However, despite efforts to enhance teachers’ digital competences, questions arise regarding whether these initiatives adequately prepare teachers for an education environment infused with AI technology.

A recent global survey, involving more than 450 schools and universities, uncovered a concerning trend: less than 10% of these institutions have established institutional policies or formal guidance regarding the use of AI applications (UNESCO, 2023a ). Furthermore, the survey highlighted a slow progress in equipping teachers with the necessary digital competence related to AI. To address this gap, recommendations put forward suggest providing teachers and students with guidance and support in understanding emerging changes accompanied by AI (Holmes et al., 2022 ; UNESCO, 2023b ). This need is emphasized by various studies that underscore teachers’ limited understanding of AI and consequently of how it could be used in education (Hrastinski et al., 2019 ; Ng et al., 2023 ; Velander et al., 2023 ).

Some authors argue that AI-related knowledge, skills, and attitudes can be built on and expand existing digital competences (European Commission, 2022 ; Heck et al., 2021 ; Ng et al., 2023 ; Vuorikari et al., 2022 ). Therefore, the relation between the two is intricate, suggesting that digital competence is a fundamental prerequisite for effective AI adoption (Ng et al., 2023 ; Velander et al., 2023 ). Nevertheless, and although there is a substantial amount of literature regarding the relation between teachers’ digital competence and their attitudes of trust regarding the integration of digital technologies into their teaching practices (Hatlevik, 2017 ; Tzafilkou et al., 2023 ), there is a research gap when it comes to exploring the relation between digital competence, knowledge of AI and trust in AI adoption.

1.2 Teachers’ trust in AI

As mentioned above, few studies have explored K-12 teachers’ trust in integrating AI based technologies into their teaching practices (Ayanwale et al., 2022 ; Kim & Kwon, 2023 ; Nazaretsky et al., 2021 , 2022 ; Su et al., 2023 ). Understanding the factors that affect trust and reduce distrust is critical, particularly given the crucial role that teachers play in the decision-making processes regarding AI in education (Nazaretsky et al., 2021 ). In addition, doing so may contribute to inform policy making within the field.

Qin et al. ( 2020 ) conducted a user-focused study on trust in AI-based educational systems and found several influential factors. These factors included technological elements (such as system functionality and interpretability), contextual factors (including data management and teacher competence), and personal factors (such as cognitive ability and teacher interaction preferences). Crockett et al. ( 2020 ) compared the perspectives on AI between the general public and individuals who had studied computer science at tertiary level. Their findings indicate that individuals with specific education in computer science had a significantly increased perception of risk associated with AI applications compared to the public. This contrast highlights the critical role of education in improving public trust and understanding of AI.

Recently, Nazaretsky et al. ( 2021 ) have made three contributions to this area. Initially, they investigated the interactive attitudes of teachers towards AI technologies, highlighting teachers’ reluctance to accept AI-based recommendations that clash with their pre-existing knowledge. This study presented preliminary findings emphasizing the crucial significance of gaining a more profound comprehension of teacher-AI interactions for more effective use of AI educational technologies in K-12 education. Following this, they proposed a new tool to measure teachers’ trust on AI educational technology, proving its validity. Through conducting exploratory factor analysis, they identified eight key factors that impact teachers’ trust in AI (Nazaretsky et al., 2022 , 2022 ). They found that teachers were positive about the benefits of AI-based educational technology and confident in their ability to implement it effectively in the classroom. However, they expressed concerns about the need for significant pedagogical changes when adopting this technology, which could potentially increase their workload. In addition, teachers appreciated methods aimed at increasing trust in AI and didn’t worry much about the prospect of being replaced by AI. They also highlighted teachers had more trust in guidance provided by human experts or peer educators than by AI. Their concerns related to the perceived lack of transparency in how AI formulates decisions, with the belief that one of the main limitations of AI in the context of K-12 education is its lack of human qualities such as emotion and intuition. Then, to promote teachers’ trust in AI, they presented a professional development plan for teachers that focused on expanding their theoretical and practical knowledge of AI in the educational field. This research also highlighted the importance of increasing teachers’ familiarity with AI to improve trust (Nazaretsky et al., 2022 ).

Ayanwale et al. ( 2022 ) studied the factors affecting the behavioral intention and readiness of teachers to teach AI. They found that anxiety was not a significant predictor of behavioral attitude and that the extent to which teachers recognize the value of AI significantly influences it. Other factors affecting teachers’ intention and readiness pertained to attitude, confidence, and relevance. Authors concluded that knowledge of AI is essential to build confidence and trust.

Chounta et al. ( 2022 ) found no correlation between professional experience and the opinions of teachers regarding the use of AI in education. Almost half of the teachers participating in their study had limited knowledge regarding AI but exhibited positive attitudes towards incorporating it in education. Teachers considered AI to act as an assistance in terms of designing learning and reviewing homework assignments. Conversely, they highlighted their apprehensions regarding the effort they would need to invest in mastering the proper usage of AI technologies and potential trust issues. Trust was found to be a crucial factor impacting the level of adoption of AI in the study by Choi et al. ( 2023 ).

Previous studies have attempted to explain what may affect teachers’ trust in AI. However, most of the research has concentrated on factors such as the effectiveness and comprehensibility of AI tools, changes in teaching approaches, and the transparency of these tools. There is limited research exploring possible differences on trust in AI based on other variables. Wang et al. ( 2023 ) found that teachers of different genders exhibited no significant differences in AI readiness. When studying differences related to the age, sex and years of experience of teachers evaluating a summary generated by ChatGPT, Vázquez-Cano et al. ( 2023 ) found that results were independent of these variables. Both studies stress the need for more studies that can produce comparable results and enlarge the current body of knowledge.

2 Materials and methods

2.1 the instrument.

This study used a quantitative approach, with an instrument developed by Nazaretsky et al. ( 2022 ) to investigate teachers’ trust in AI (TAI) and the factors influencing it. The instrument consists of 24 items across eight factors: Self-efficacy (F1), AI-based vs. Human advice/recommendation (F2), Anxieties related to using AI (F3), AI lack of human characteristics (F4), Perceived benefits of AI (F5), Preferred means to increase Trust in AI (F6), AI perceived lack of transparency (F7) and Required shift in pedagogy to adopt AI (F8). Respondents are asked to position themselves against them using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). In addition, the instrument included three other sections. One to collect demographic information, including age, sex, teaching experience and ISCED level taught; another to collect the participants’ perceptions of their knowledge of AI (KAI) and another of their digital competence (DC) proficiency level. KAI was measured using a six-point scale proposed by Chounta et al. ( 2022 ) with the following options: 1-I have never heard of AI; 2-Not sure what AI is; 3-I have limited knowledge about AI; 4-I know what AI is; 5-I know a lot about AI and 6-I am an expert in AI. DC was measured using the DigCompEdu proficiency scale (Redecker, 2017 ), which includes the following levels: A1-Newcomer, A2-Explorer, B1-Integrator, B2-Expert, C1-Leader, C2-Pioneer. Participants were familiar with this proficiency scale and the meaning of each label, as DigCompEdu was adopted as a foundational document for the national strategic plan for the digital transition in education approved by the Portuguese government in 2020. Within this plan, 92% of the Portuguese teachers self-assessed their digital competence, which was then translated into this same scale (Lucas & Bem-Haja, 2021 ) and used to allocate teachers to training programs (currently ongoing).

2.1.1 The translation of the instrument

To ensure the content validity of the Portuguese version of the questionnaire, we followed Brislin’s translation model ( 1986 ) and MAPI’s ( 2005 ) guidelines. Initially, two proficient Portuguese native speakers, fluent in English translated the instrument. Subsequently, an independent translator, fluent in Portuguese but with English as their native language, back translated the instrument. The back-translation was thoroughly reviewed and compared with the original version. Disparities were examined by the original translators and discussed with an expert panel. A unified version was agreed upon and tested with six teachers in a pilot study. Feedback from the pilot study was reviewed, and minor adjustments, such as normalizing expressions in Portuguese, were made.

2.2 Sample and procedure

The cross-sectional study surveyed a sample of 211 teachers from primary and secondary schools in Portugal, who were enrolled in a MOOC covering digital assessment related topics. The demographic data of the sample are shown in Table  1 .

As part of one of the MOOC learning modules, in which the topic of datafication and AI were addressed, teachers were invited to test their attitudes towards the adoption of AI based tools. The intention of the survey was fully disclosed. Participation was voluntary, anonymous, and fully consented.

2.3 Factorial validity and reliability

The Mardia tests of multivariate skewness and kurtosis were conducted on a dataset consisting of 211 observations across 24 variables. The results revealed substantial departures from normality: the test indicated a skewness of 4669.72 (p < = 2.1e-121), and a kurtosis of 20.2 (p < = 0). Additionally, a small sample skewness of 4741.48 was observed with a probability of p < = 2.2e -128 .

Due to the non-normal distribution assumption of the data, the selection of the weighted least square mean and variance adjusted (WLSMV) estimator within the structural equation modelling (SEM) framework was made when conducting confirmatory factor analysis (CFA). The analysis results indicate that the comparative fit index (CFI) and the Tucker–Lewis index (TLI) exhibit values of 0.998 and 0.997, respectively, demonstrating an exceptionally high level of model fit with the observed data. The robust root mean square error of approximation (RMSEA) stands at 0.051, falling within the range indicative of a favorable fit, with a 90% confidence interval spanning from 0.040 to 0.062. Furthermore, the Standardized Root Mean Square Residual (SRMR) is measured at 0.067, further underscoring the model’s commendable fit.

Table  2 reveals eight latent factors (F1-F8) linked to various observed indicators (Q16-Q18, Q23-24, Q13-Q15, Q8-Q11, Q1-Q7, Q20-Q22, Q12, and Q19). All the factor loadings are statistically significant, surpassing 0.4, except for item Q14. This meets the threshold for factor loading for a sample size greater than 200 (Hair et al., 2019 ), indicating a secure association between the latent factors and the observed variables. Considering the value of lambda achieved by item Q14, the results of F3 should be read with caution.

Based on the presented reliability coefficients, F1, F2, F5 and F6 demonstrate a notably high level of reliability, as evidenced by their Cronbach’s alpha coefficients, all of which exceed 0.8. F3 and F4 also exhibit alpha coefficients surpassing the threshold of 0.6 (Robinson et al., 1991 ), indicating acceptable reliability for the purpose of exploratory research. This observation highlights the strong internal consistency of the findings.

To calculate the score for each factor, a weighted average was performed considering the factor loadings of each constituent item. The global TAI score derives from the sum of scores of all factors.

2.4 Data analysis

The analyses were conducted using R and JASP. Multivariate skewness and kurtosis were evaluated using Mardia tests. Afterwards, CFA was used with the WLSMV estimator in the SEM framework to assess the factorial validity of the instrument. The overall fit of the factor model was determined by indices including CFI, TLI, RMSEA, and SRMR. Internal consistency was further examined via a reliability analysis.

A series of general linear regression analyses were carried out to assess the relations between TAI, and age, sex, teaching experience and ISCED level. These variables were then integrated into a predictive model to determine if it could provide a satisfactory level of predictive performance for TAI. In order to achieve this, a machine learning regression approach was used, with boosting as the ensemble method and K-fold cross-validation. Network analysis was employed as a pertinent model to investigate the relations among the variables TAI, KAI and DC. To determine if these direct relations persisted while controlling for the impact of other variables, partial correlation network analysis was conducted. Additionally, three mediation analyses were conducted to explore whether the relation between TAI and DC was mediated by KAI.

3.1 Relation between TAI vs. KAI vs. DC

A network analysis was performed as a correlational model to assess the relation between the variables TAI, KAI and DC. The resulting network is shown in Fig.  1 .

Network with correlations between TAI, KAI and DC

As shown in Fig.  1 , a statistically significant positive correlation was observed between all variables. However, the relation between TAI and DC, although statistically significant, can be characterized as weak ( r  = 0.17, p  = 0.01; see Cohen, 1988 ). KAI emerges as a central factor within the network, with values above 1 for the betweenness, closeness and expected influence metrics. Given that TAI is assessed using 8 different factors, and recognizing the potential for differential effects that could justify the observed result, these individual factors were integrated into the model and the overall result was removed. The results of this detailed analysis are presented in the network diagram on the right-hand side of Fig.  2 .

Left network: Bivariate correlations between the eight factors of TAI, KAI and CD. Right network: Partial correlations between the eight factors of TAI, KAI and CD. Note:  F1: Self-efficacy; F2: AI-based vs. Human advice/recommendation; F3: Anxieties related to using AI; F4: AI lack of human characteristics; F5: Perceived benefits of AI; F6: Preferred means to increase Trust in AI; F7: AI perceived lack of transparency; F8 Required shift in pedagogy to adopt AI

The results derived from the left-hand side network in Fig.  2 show that F1 and F6 are associated with DC and highlight the existence of a significant relation between DC and TAI. Interestingly, the robust correlation observed in Fig.  1 between KAI and TAI appears to be supported by F1, F2, F6 and F5, while no relation between KAI and F3, F4 and F7 seems to exist. It is important to note that the absence of edges between nodes within the network implies the absence of significant relations.

To verify whether these direct relations persist after controlling for the influence of other variables, a network analysis with partial correlations was carried out (see the right-hand side network in Fig.  2 ). The results obtained indicate that the relation between KAI and F2 has disappeared, but a relation between KAI and F3 has emerged. However, the most striking result is that when the influence of other variables is controlled for, the relations between DC and F1 and F6 disappear, while the strong relation between DC and KAI persists. This result suggests that the previously observed relation between TAI factors and DC appears to be mediated by KAI. To validate this assumption, three mediations were performed: one for TAI and the other two for its F1 and F6. The analysis assessing whether KAI mediates the relation between DC and TAI (overall) is shown in Fig.  3 .

Path diagram of the mediation model with TAI as a dependent variable, DC as predictor and KAI as mediator

The analysis shows that KAI acts as a significant mediator in the apparent relation between DC and TAI. Specifically, the indirect effect (a*b) is quantified at 0.648, with a 95% CI ranging from 0.364 to 0.969. In fact, when examining Fig.  3 , it becomes clear that in the absence of KAI, the significant relation between DC and TAI ceases to exist. The results show that 88% of the total relation, denoted by c’, can be attributed to mediation. Conceptually, this suggests that higher levels of DC are associated with increased levels of KAI, and increased levels of KAI correspond to increased levels of TAI. Similar patterns emerge from the mediation analyses for F1 (a*b = 0.185, 95% CI[0.107, 0.272]) and 6 (a*b = 0.146, 95% CI[0.064, 0.235]). KAI consistently plays a mediating role, meaning that higher DC scores are associated with higher KAI scores, and higher KAI scores are associated with higher levels of TAI F1 and F6. The cumulative results emphasise that KAI is a robust and substantial predictor of TAI.

3.2 Relation between TAI, age, sex, teaching experience and ISCED levels

To assess the relation between TAI, age, sex, teaching experience and ISCED level four general linear models were carried out, one for each independent variable. The descriptive results of these analyses are shown in Fig.  4 .

Relation between TAI, age, sex, teaching experience and ISCED levels

According to Fig.  4 , there appears to be no significant relation between TAI and the four variables analyzed. Inferential results show a lack of significant relation between TAI and age, as indicated by F(1, 210) = 0.186, p  = 0.666, η² p  = 0.0001. Similarly, no significant relation was found between TAI and sex, as indicated by F(1, 210) = 1.231, p  = 0.269, η² p  = 0.0006, TAI and teaching experience, as indicated by F(1, 210) = 0.048, p  = 0.827, η² p  < 0.0001, or TAI and ISCED level, as indicated by F(1, 210) = 0.118, p  = 0.889, η² p  = 0.0001.

Although these variables do not show individual relations with TAI, it was worth investigating whether their combined inclusion in a predictive model gave satisfactory predictive performance for TAI. To this end, a machine learning regression approach was adopted using boosting as the ensemble method and K-fold as a cross-validation procedure. This analysis yielded poor results, with the most effective model accounting for only 1.6% of the variance in TAI and a MAPE equal to 101.8, indicating a tendency for the model to overestimate predictions on average. Consequently, it can be said that TAI is independent of age, sex, teaching experience and ISCED level in this sample of teachers.

It is important to note that in contrast to the lack of relation between TAI and these variables, the results show a significant negative relation between DC and age, as indicated by F(1, 210) = 5.031, p  = 0.026, η² p  = 0.024. There is also evidence of a male advantage in DC scores, as indicated by F(1, 210) = 4.413, p  = 0.037, η² p  = 0.021. These results conform to the ones typically found in the related literature (Cabero-Almenara et al., 2022 ; Lucas et al., 2021 ), which provides robustness to our findings and suggest they do not result from a biased sample.

4 Discussion

The present study sought to identify the variables that may influence K-12 teachers’ TAI with the following research questions:

RQ1: What is the relation between TAI, KAI and DC?

RQ2: What is the relation between TAI, age, sex, teaching experience and ISCED levels?

4.1 What is the relation between TAI, KAI and DC?

The study results show that there is a statistically significant positive correlation between all three variables and that KAI is a robust and substantial predictor of TAI. The relation between KAI and TAI is consistent with the findings of Kaya et al. ( 2022 ), who observed that individuals’ self-assessed knowledge levels regarding AI were linked to their attitudes toward it. Those with a better understanding of technological innovations tended to have greater awareness of their practical applications and value, and consequently, a more positive outlook on AI.

However, it becomes clear that in the absence of KAI, the significant relation between DC and TAI ceases to exist. This result supports the perspective of Polak et al. ( 2022 ), who argue that DC is a prerequisite for the acquisition of KAI. However, it is noteworthy that teachers with different levels of DC do not show significant differences in their attitudes towards AI. This highlights the inadequacy of traditional definitions of DC to address the rapid advances in AI. As a result, it is recommended that existing DC frameworks be reviewed and updated to incorporate relevant AI concepts (Ng et al., 2023 ; Polak et al., 2022 ). Thus, although DC frameworks and related policies have been continually adjusted to incorporate AI related concepts (e.g. Vuorikari et al., 2022 ), the absence of a direct relation between TAI and DC indicates that despite the widespread recognition of the significance and potential of AI education, its full potential remains untapped (Luckin & Holmes, 2016 ). While current research highlights the importance of teachers having sufficient DC to integrate AI into subject teaching (Casal-Otero et al., 2023 ; Ng et al., 2023 ), the challenge lies in the fact that AI literacy has not been sufficiently integrated into the definition of DC (Velander et al., 2023 ). As highlighted by UNESCO, public policy development for AI in the education sector is still in its early phase. This makes it difficult to identify common components at this initial stage (UNESCO, 2019 ).

To accurately assess the relations among the eight factors of TAI, KAI, and DC, a network analysis was conducted. Our bivariate correlation results indicate a close relation between KAI and TAI, supported by F1, F2, F6, and F5. In contrast, no relation was observed between KAI and F3, F4, and F7. In Nazaretsky et al. ( 2022 ), the eight factors measured were categorized into three domains: Perceived benefits of AI in an educational setting (F5), reasons for not trusting AI diagnosis (F3, F4, F7), working alongside AI to improve pedagogy (F1, F2, F6, F8). It was observed that these three domains correspond to the three key elements that characterize trust as identified by Vereschak et al. ( 2021 ): vulnerability (F3, F4, F7), positive expectations (F5), and attitude (F1, F2, F6, F8). Therefore, from a purely bivariate correlations perspective, KAI is associated with the positive expectations (Perceived benefits of AI in an educational setting) and attitudes (Working alongside AI to improve pedagogy). However, factors related to the vulnerability (Reasons for not trusting AI diagnosis) do not appear to be associated with KAI.

The connection between F1, F2, F6, and F5 imply that attitude and positive expectation elements are intertwined, namely the confidence element (Vereschak et al., 2021 ). Lucas et al. ( 2021 ) employed machine learning and multiple linear regression techniques to demonstrate that teachers’ confidence is a predictor of their digital competence. Additionally, it has been demonstrated by different studies (Hatlevik, 2017 ; Tzafilkou et al., 2023 ) that confidence and digital competence have a positive correlation. Consequently, the relation between KAI and F1, F2, F6, and F5 is well-established: in AI is positively correlated with KAI.

Vereschak et al. ( 2021 ) suggest that vulnerability is a crucial component in building trust. The intention behind creating this vulnerability is to immerse the participants in a state of fragility where they can realize the importance of their own decisions. Nevertheless, this vulnerability includes uncertain elements, and sometimes the possibility of results can be estimated, while other times it remains unquantifiable. Clearly, in AI, there is uncertainty due to the unpredictability of the world and the boundaries of human knowledge and capabilities. As a result, teachers are not capable of objectively evaluating the suitability of implementing AI (Bentley et al., 2023 ). This can cause a swing between complete reluctance and excessive confidence (Casal-Otero et al., 2023 ). Therefore, gauging the relation between KAI and this intricate, multifaceted and unstable vulnerability cannot be done justice by solely relying on bivariate correlations.

To ensure precision and independence in our results, we conducted a partial correlation network analysis to control for the influence of other variables. The analysis revealed a positive correlation between KAI and F3, which pertains to Anxieties Related to Using AI-based EdTech. Increased anxiety towards the integration of AI was associated with elevated levels of KAI. This phenomenon corresponds with the findings reported by Crockett et al. ( 2020 ), which indicate that individuals with specific training in computer science, in contrast to the general public, have a higher perception of risk in relation to AI applications.

The description of this phenomenon requires, above all, recognizing that the presence of AI carries potential hazards, which do not necessarily equate to people holding an adverse view or stopping the use of related software (Kaya et al., 2022 ). However, these risks and resulting anxieties are similar to past computer anxieties, including fears of job displacement, learning anxiety, and privacy invasion. However, distinct anxieties have also emerged, including fears of artificial consciousness, lack of transparency, moral transgression, which have yet to be fully explored (Li & Huang, 2020 ). Concerns arising from emerging risks are distinct from computer anxiety since all computer ethical standards are based on human norms and computers are merely tools. However, present-day AI systems can act as ethical agents, potentially creating unprecedented ethical dilemmas in human-machine interactions (Li & Huang, 2020 ). Therefore, currently, if an exclusively ethical discussion lacks the backing of ample AI knowledge, is insufficient to promote deep understanding and application throughout the entire AI lifecycle (UNESCO, 2022 ). Therefore, we argue that the idea that heightened computer experience reduces anxiety (Heinssen et al., 1987 ), which was often valid during the computer era, may not necessarily apply to the current stage of AI development. In the current context, it is crucial to have sufficient knowledge and understanding of AI to be able to identify potential dangers associated with it. Paradoxically, as individuals gain deeper insights into AI, they may face increased levels of uncertainty, which could potentially worsen the anxiety surrounding AI.

Our results are in line with the findings of Nazaretsky et al. ( 2022 ), which suggest that the substitution of teachers by AI is not the foremost concern of teachers. Nevertheless, they highlight that this outcome deviates from the opposite conclusions of public opinion research. This viewpoint is supported by Chounta et al. ( 2022 ), which indicates that while there is a popular assumption that the implementation of AI in the workplace could lead to human unemployment, this concern seems to be of less importance for K-12 teachers. Nonetheless, according to an evaluation of the possible influence of AI on teachers’ existing tasks by the European Commission, many tasks appear to be amenable to straightforward automation. One possible explanation they propose is that computer technology has advanced to the point where it can perform certain tasks that require a higher level of human cognition, or, in other words, that teachers are currently engaged in relatively mechanistic tasks within the current educational system (Tuomi, 2018 ).

We compare the attitude of teachers to societal attitudes, not to suggest that teachers should be replaced. As emphasized by the European Commission ( 2022 ), AI is not meant to replace teachers, but rather to support them in their work. Nevertheless, it is worth considering the reasons for teachers’ low anxiety. We should consider whether this is due to their limited understanding of KAI and AI potential risks, or whether it is due to their full understanding of the learning experiences that AI systems themselves cannot provide, thus alleviating their anxiety.

4.2 What is the relation between TAI, age, sex, teaching experience and ISCED level?

Our data shows no relation between teachers’ TAI and factors such as age, sex, teaching experience and ISCED level. This result is consistent with previous studies (Kaya et al., 2022 ; Vázquez-Cano et al., 2023 ; Wang et al., 2023 ), which suggests that age, sex and educational level are not predictors of attitudes towards AI. Moreover, Chounta et al. ( 2022 ) highlights the absence of a noteworthy statistical correlation between professional experience and the opinions of teachers regarding the use of AI in education.

Considering that we are in the initial phase of AI in education, when comparing TAI with DC data, it is worth to reflect on the early incorporation of computers in education. Previous studies have also highlighted the impact of personal variables such as sex and age on attitudes and anxieties towards computer applications, and the conclusions in that moments are consistent with the TAI data. For example, Loyd and Gressard ( 1984 ) found that students from various age groups exhibited no significant differences in their attitudes towards computers. Heinssen et al. ( 1987 ) highlighted the lack of consistent sex differences in computer anxiety. Moreover, the study conducted by Pope-Davis and Twing ( 1991 ) discovered no sex interaction effect, although men had a slightly stronger tendency towards positive average computer attitudes. Notably, Dyck and Smither ( 1994 ) explicitly indicated that, when computer experience was controlled for, no apparent sex differences existed in computer anxiety and computer attitudes.

Therefore, the existing trend of TAI among teachers being independent of their age, sex, teaching experience and ISCED level is explicable, resembling the initial phases of computer integration into education. As highlighted by UNESCO, despite several countries implementing regulations to assimilate AI into the syllabus, its widespread incorporation in K-12 education has not yet been achieved (UNESCO, 2022 ). As a novel conception, a significant number of teachers have a limited understanding of AI implementation (Crompton et al., 2022 ).

4.3 Limitations and future directions

Based on the previous discussion, it’s evident that without sufficient KAI, a substantial connection between DC and TAI cannot be established. In other words, in the context of future research in AI in education, as well as in the definition of public policies for teacher training, the enhancement of teachers’ KAI levels holds the key potential to improve their TAI. This relation is supported by Qin et al. ( 2020 ), which posits that individual factors play a critical role in the development of trust. These factors comprise cognitive understanding of AI, tendencies towards autonomy, teacher interaction preferences, and recognition of the nature of learning. Nazaretsky et al. ( 2022 ) also highlight the paramount importance of enhancing teachers’ theoretical and practical knowledge in the field of education to cultivate trust in AI, especially in the context of K-12 education.

However, raising teachers’ KAI levels may imply moving beyond short-term planning and instead envision future scenarios to explore potential developments in education (Seufert et al., 2021 ). It is imperative to recognize that improving KAI may result in an increase in teachers’ anxiety levels about AI, given its positive correlation with F3. However, until uncertainties surrounding AI in education are fully clarified and/or diluted through the gradual incorporation of AI into educational routines and practices, this phenomenon should be viewed as an unavoidable reality. As a result, we have to admit the possibility that teachers may have both positive expectations as well as anxieties towards AI. In this respect, comprehensive policies and educational initiatives are essential to equip teachers with the knowledge and understanding necessary to navigate the complexities of AI in education effectively.

Nevertheless, this study has some inherent limitations. Firstly, it is challenging to generalize the findings as the research sample solely consists of Portuguese teachers from primary and secondary schools. Furthermore, the predictive effects of age and teaching experience on TAI may be biased due to the limited number of young teachers included in the sample. Nevertheless, in Portugal, as in many other European countries, more than half of the in-service teachers are aged 50 years or older (OECD, 2019 ). Future studies involving other samples, inclusive from other educational contexts and younger teachers, could be of interest. This would facilitate the comparison of findings across different nations and educational settings. Additionally, it is important to note that the perception of KAI and DC relied on a single self-assessment question, which may lead to teachers’ overestimation or underestimation.

5 Conclusions

The study aimed to provide practical implications for teacher preparation and in-service professional development regarding AI and implementation of AI tools and processes in K-12 education. The findings reveal no significant relation between TAI and variables such as age, sex, teaching experience and ISCED level of teachers. A statistically significant positive relation was observed among KAI, TAI and DC. However, KAI acts as a crucial mediator in the relation between DC and TAI. More specifically, the significant relation between DC and TAI disappears in the absence of KAI. Therefore, despite the ongoing adaptation of DC frameworks and related policies, the absence of a direct relation between TAI and DC suggests that the full potential and importance of AI in K-12 education has yet to be fully realized. In summary, this study highlights the significance of improving teachers’ TAI during the current stages of AI interaction in K-12 education. Such improvement can be accomplished by augmenting teachers’ KAI.

Data availability

All data and materials support the published claims and comply with field standards.

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This work is financially supported by National Funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., under the project 2022.14365.BD, 2021.03379.CEECIND and UIDB/00194/2020.

Open access funding provided by FCT|FCCN (b-on). This work is financially supported by National Funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., under the project 2021.03379.CEECIND, 2022.14365.BD and UIDB/00194/2020.

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Want to Get Younger Students Started on Esports? Try Minecraft Education.

Lindy Hockenbary is the founder of and the Educational Technology Consultant for InTECHgrated Professional Development.

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